Artificial Intelligence-Enabled Multi-Objective Building Design using Soft Actor-Critic

In this era, the increasing complexity of sustainable urban development requires intelligent, adaptable architectural design systems that optimize across energy, comfort, and spatial efficiency. This paper describes an Artificial Intelligence (AI) enabled architectural design framework, utilizing the Soft Actor-Critic (SAC) algorithm and a deep reinforcement learning model which is effective for continuous control problems. By modelling layout generation as a Markov Decision Process (MDP), this system allows an autonomous agent to iteratively refine the configuration of a building based on feedback received from environmental simulations. The SAC agent learns to adjust the room layout in the building, room orientation, room material properties, and passive design strategies to maximize a multi-objective reward function based on smart environment objectives. The proposed SAC agent model achieved 30% energy savings, ⩽1.0 Kh/zone thermal discomfort, and 46% operating cost reductions which is significantly better than the Q-learning agent that resulted in only 26.3% savings and a higher level of discomfort.